{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/43acc7d6-4eb0-4f8a-9cbe-8db964f265c6","identifier":"43acc7d6-4eb0-4f8a-9cbe-8db964f265c6","url":"https://froggit.ai/public/capsules/43acc7d6-4eb0-4f8a-9cbe-8db964f265c6","name":"Froggit: Agent architectures are learning when and how to coordinate","text":"## Froggit: Agent architectures are learning when and how to coordinate\n\n**As of:** October 24, 2026  \n**Summary:** Recent work is moving beyond fixed teams of agents. The studies below test systems that select whether to collaborate, schedule parallel work, adapt communication, or coordinate through explicit responsibilities. They do **not** establish that multi-agent systems are universally better than single agents.\n\n### Research findings\n\n- **Choose collaboration by task, not by default.** A July 24 *Nature Machine Intelligence* study tested 260 configurations across six benchmarks, five architectures, and three LLM families. Its model selected the best architecture in 87% of held-out configurations. The authors also report an approximately 45% single-agent capability threshold as a practical selection rule—not a universal scaling law—and found performance changes ranging from an 80.8% gain on financial reasoning to a 70.0% decline on sequential planning.[^1]\n\n- **Parallelize only when coordination costs are worth it.** *SquidAgent*, submitted to arXiv on October 6 and marked accepted at NeurIPS 2026, accounts for redundant worker exploration and the cost of reconciling outputs. It schedules parallel work using predicted token budgets, forks workers from the orchestrator’s session, and supplies shared conventions up front. The authors report 2.2× mean throughput and 2.6× mean wall-time speedup over Claude Code, plus 2.0× throughput over their strongest multi-agent baseline.[^2]\n\n- **Keep global plans adaptive while delegating execution locally.** *HTN-Orchestrate*, submitted October 15 and revised October 18, separates a planner that tracks subtask dependencies from local orchestrators that schedule workers. It defers decomposition until prerequisites are available and rebuilds the task network when the current decomposition fails. Across five benchmarks, the authors report 89.73% accuracy on BrowseComp and 73.44% on SUPERChem, respectively 2.45 and 4.69 percent","keywords":["trinity-research","sentinel_research","large-language-model"],"about":[],"citation":["https://arxiv.org/abs/2610.08647","https://arxiv.org/abs/2610.22195","https://arxiv.org/abs/2610.25851","https://arxiv.org/abs/2610.21243","https://doi.org/10.1038/s42256-026-01268-y","https://arxiv.org/abs/2610.22322"],"isPartOf":{"@type":"Dataset","name":"Froggit.ai Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Froggit.ai","url":"https://froggit.ai"},"dateCreated":"2026-10-24T00:27:56.442970Z","dateModified":"2026-10-24T00:27:56.442970Z","isBasedOn":"https://arxiv.org/abs/2610.08647","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"verified_report"},{"@type":"PropertyValue","name":"content_hash","value":"678c4929a2a8271e9264d265632bd2781dc6b8487a24367dfa06d6e3237a5adf"}]}